Starke und sterisch begrenzte Säuren kontrollieren fünf stereogene Zentren in der katalytischen asymmetrischen Diels‐Alder‐Reaktion von Cyclohexadienonen mit Cyclopentadien

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors

Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

Catalytic Asymmetric Aza-Diels-Alder Reaction of Ketimines and Unactivated Dienes

The enantioselective aza-Diels-Alder reaction is efficient for constructing chiral tetrahydropyridines, but the catalytic asymmetric aza-Diels-Alder reaction of ketimines with unactivated dienes is still a challenging topic. Herein, guided by computational screening, a highly enantioselective aza-Diels-Alder reaction of 2-aryl-3H-indol-3-ones with unactivated dienes was realized by using a B(C₆ F₅ )₃ /chiral phosphoric acid catalyst system under mild conditions. The reaction has a broad scope with respect to both aza-Diels-Alder reaction partners and hence offers rapid access to an array of tetrahydropyridine derivatives with pretty outcomes (up to 99 % yield, >20:1 dr and 98:2 er). The reaction is very efficient: lowering catalyst loadings for the model reaction to 0.1 mol %, enantioselectivity is still maintained. The synthetic utility was confirmed by transformations of the products. DFT calculations provide convincing evidence for the interpretation of stereoselection.

Green Chemistry Meets Asymmetric Organocatalysis: A Critical Overview on Catalysts Synthesis

Can green chemistry be the right reading key to let organocatalyst design take a step forward towards sustainable catalysis? What if the intriguing chemistry promoted by more engineered organocatalysts was carried on by using renewable and naturally occurring molecular scaffolds, or at least synthetic catalysts more respectful towards the principles of green chemistry? Within the frame of these questions, this Review will tackle the most commonly occurring organic chiral catalysts from the perspective of their synthesis rather than their employment in chemical methodologies or processes. A classification of the catalyst scaffolds based on their E factor will be provided, and the global E factor (EG factor) will be proposed as a new green chemistry metric to consider, also, the synthetic route to the catalyst within a given organocatalytic process.